Volume 119 - Issue 2

Feeding the world’s population in a sustainable manner is one of the key challenges facing the future of global agriculture. The recent removal of the milk quota regime in the European Union has prompted an expansionary phase in dairy farming, especially in Ireland. Achieving this expansion in a sustainable manner is crucial to the long-term survival and success of the Irish dairy sector. In this paper we examine the sustainability of Irish dairy farming, defining ‘sustainability’ as economically profitable, environmentally friendly and socially efficient. A typology of Irish dairy farms has been created using data on profitability, environmental efficiency and social integration derived from the Teagasc National Farm Survey. Economic, social and environmental performance indicators were determined and aggregated and then used in a multivariate analysis for the identification and classification of farm clusters. The purpose of this study to classify Irish dairy farms using performance indicators, thereby, assisting policy makers in identifying patterns in farm performance with a view to formulating more targeted policies. Two of the three clusters elicited from the analysis were similar in regards to their respective indicator scores. However, the remaining cluster was found to perform poorly in comparison. The results indicate a clear distinction...

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Using Monte Carlo simulations, the impact of diff erent levels of risks on indemnification through an income stabilisation tool is investigated at the sector level. The presented approach, using the IACS database, allows analyses of diff erences across farms with respect to farm type and farm size, applying average-based approaches. Such preliminary information is useful for policy makers responsible for the design and introduction of measures to tackle income risk issues and to identify potential beneficiary groups among farmers. The analysis shows that on average 25 per cent of farms would be indemnified annually, the majority in fruit production, the dairy sector and hop production. Mixed farm types, with a share of 34 per cent, receive only 15 per cent of the total sum of indemnity. However, if EUR 12,000 of average income is set as the threshold for participation in such a tool, only 6 per cent of farms participate and only 13.3 per cent of them would be indemnified. Indemnity at farm level would range between EUR 82 and 40,870. Taking into account all farms in the sector, the average indemnity is EUR 918 per farm and almost EUR 13,500 for the second case.

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Given the strong dependence of its economic results on natural factors, agriculture is characterised by high exposure to risk. This paper explores the relationship between the prevailing ‘model of agriculture’ in a country and methods of risk management (in particular, insurance schemes). The Czech Republic and Poland are post-socialist countries which are characterised by different models of agricultural development. While agriculture in the Czech Republic is oriented to industrial farming with large farms, Polish agriculture has a bipolar structure that includes both small, family-owned farms and large agricultural holdings. Various approaches to agricultural insurance schemes may arise from the contrasting models of agriculture, and substantial differences in both the demand and supply sides of the crop and livestock insurance markets indicate different policy approaches to the role of agriculture in the economies of the two countries. In both the Czech Republic and Poland, policy options for farm risk management should consider the balance between budget flexibility and the criterion of efficiency (from the perspective of insurers).

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In the summer of 2014, Russia imposed a food embargo on most agricultural products from countries that supported the anti-Russian sanctions. In this study we use vector autoregression and neural network modelling to assess the eff ect of the embargo on the bilateral trade relations between the European Union (EU) (using the example of Hungary as an EU Member State) and Russia. In particular, the changes in the dynamics of Hungary’s aggregate agricultural exports in response to the shock of the embargo, as well as to Russia’s imports of products banned under the embargo, are analysed. The work also looks at the eff ectiveness of the introduction of the embargo with the aim of implementing import substitution policies and supporting domestic producers. Our results show the ineff ectiveness of the Russian import substitution policy and the negative eff ects on both Russian and Hungarian parties.

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In this paper, we employ a novel, network analysis based approach to gain new insights with respect to the changes in the structure of intra-European Union (EU) milk product trade between 2001 and 2012. Several network indices are computed to assess the relative importance of the countries from a number of perspectives. The results emphasise that the trade network has become denser, yet its overall centralisation slightly decreased during the period. While the impacts of the 2004 EU enlargement are clearly visible, the effects of the 2008 financial crisis are less evident. Integration of countries that joined the EU in 2004 or 2007 (the so-called New Member States, NMS) is only partial, and depends on the category of milk product considered. Although the number of NMS trade relations increased constantly between 2001 and 2012, the relative importance of most of them did not change. A significant exception is Poland, which became one of the most important exporting countries.

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This paper investigates the symmetry of impact from three main food scare events on both the upstream and downstream price transmissions in the Chinese pork market through monthly data from 2001 to 2014. Based on a theoretical model, we firstly estimate the VAR systems for pork retail price and price transmissions in different links, and then plot the impulse response function and dynamic multiplier function respectively for endogenous substitute good price and exogenous food scare events. Empirical results indicate the asymmetry of price transmission in the Chinese pork market, and demand and supply shocks from three food scare incidents are found to impact retail price and price transmissions differentially. In addition, shocks from the same incident on price and price transmissions are significantly different. This research provides implications for farmers, business managers and policy makers to make strategies in response to food scare events.

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A fundamental challenge in impact evaluations that rely on a quasi-experimental design is to define a control group that accurately refl ects the counterfactual situation. Our aim is to evaluate empirically the performance of a range of approaches that are widely used in economic research. In particular, we compared three diff erent types of matching algorithms (optimal, greedy and nonparametric). These techniques were applied in the evaluation of the impact of the MARENA programme (Manejo de Recursos Naturales en Cuencas Prioritarias), a natural resource management programme implemented in Honduras between 2004 and 2008. The key findings are: (a) optimal matching did not produce better-balanced matches than greedy matching; and (b) programme impact calculated from nonparametric matching regressions, such as kernel or local linear regressions, yielded more consistent outcomes. Our impact results are similar to those previously reported in the literature, and we can conclude that the MARENA programme had a significant, positive impact on beneficiaries.

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Journal Metrics

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  • Scopus SJR (2022): 0.27
  • Scopus CiteScore (2022): 2.0
  • WoS AIS (2022): 0.23
  • WoS JCI (2022): 0.37
  • ISSN (electronic): 2063-0476
  • ISSN-L 1418-2106



Publisher Name: Institute of Agricultural Economics Nonprofit Kft. (AKI)

Publisher Headquarters: Zsil utca 3-5, 1093-Budapest, Hungary

Name of Responsible Person for Publishing:        Dr. Pal Goda

Name of Responsible Person for Editing:             Dr. Attila Jambor

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

The publication cost of the journal is supported by the Hungarian Academy of Sciences.

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